Title: Telecom fraud detection with big data analytics

Authors: Duygu Sinanç Terzi; Şeref Sağıroğlu; Hakan Kılınç

Addresses: Computer Engineering Department, Gazi University, Ankara, 06570, Turkey ' Computer Engineering Department, Gazi University, Ankara, 06570, Turkey ' Cyber Security Product Line and Research Centre, Netas, İstanbul, Turkey

Abstract: The rapid development in telecom has also led to an increase in fraud activities, which causes both revenue and reputation losses. For this reason, this paper proposes a new telecom fraud detection model based on behaviour deviations of users expressed through time-varying signatures. In line with the similarity of these deviations to known frauds, a suspect list has been created and reported to fraud experts for the final decision. The proposed model was developed with the MapReduce parallel programming paradigm, which provides simplicity and flexibility for large-scale applications. Finally, the model was applied on call detail records of a telecom company. The obtained results have shown that the proposed approach detects the telecom frauds with 86% success and is suitable for application into a fraud management system for real-world implementation.

Keywords: telecom fraud detection; big data analytics; signature-based user profiling; behaviour analysis.

DOI: 10.1504/IJDS.2021.121090

International Journal of Data Science, 2021 Vol.6 No.3, pp.191 - 204

Received: 01 Jul 2020
Accepted: 05 Jul 2021
Published online: 24 Feb 2022 *

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